Quantitative assessment of photic phenomena in the presbyopia-correcting intraocular lens

This was a prospective study to evaluate the feasibility of the photic phenomena test (PPT) for quantifying glare, halo, and starburst. We compared two presbyopia-correcting intraocular lenses (IOLs), the Symfony IOL and the PanOptix IOL, as well as the monofocal Clareon IOL in 111 IOL-implanted eyes of 111 patients who underwent the PPT 1 month postoperatively. The reproducibility of photic phenomena with the PPT was assessed in 39 multifocal IOL-implanted eyes of 20 patients and among the examiners. Patients with ocular diseases, except for refractive errors, were excluded. The mean values of the groups were evaluated. Bland–Altman plots were used to analyze statistical data (Easy R version 1.37; R Foundation for Statistical Computing, Vienna, Austria). The PPT reproducibility assessment revealed no fixed bias or regressive significance. Reproducibility was confirmed. The glare size did not differ significantly between the Symfony, PanOptix, and Clareon groups. The halo size was significantly larger in the Symfony group (p < 0.01) than in the PanOptix group. The halo intensity was significantly brighter in the PanOptix group (p < 0.01) than in the Symfony group. In contrast, no halos were perceived in the Clareon group. The starburst size or intensity did not differ significantly between the Symfony, PanOptix, and Clareon groups. We identified the photic phenomenon related to various IOLs.

Biases and Variability from Costly Bayesian Inference

When humans infer underlying probabilities from stochastic observations, they exhibit biases and variability that cannot be explained on the basis of sound, Bayesian manipulations of probability. This is especially salient when beliefs are updated as a function of sequential observations. We introduce a theoretical framework in which biases and variability emerge from a trade-off between Bayesian inference and the cognitive cost of carrying out probabilistic computations. We consider two forms of the cost: a precision cost and an unpredictability cost; these penalize beliefs that are less entropic and less deterministic, respectively. We apply our framework to the case of a Bernoulli variable: the bias of a coin is inferred from a sequence of coin flips. Theoretical predictions are qualitatively different depending on the form of the cost. A precision cost induces overestimation of small probabilities, on average, and a limited memory of past observations, and, consequently, a fluctuating bias. An unpredictability cost induces underestimation of small probabilities and a fixed bias that remains appreciable even for nearly unbiased observations. The case of a fair (equiprobable) coin, however, is singular, with non-trivial and slow fluctuations in the inferred bias. The proposed framework of costly Bayesian inference illustrates the richness of a `resource-rational’ (or `bounded-rational’) picture of seemingly irrational human cognition.

An image processing algorithm for detection and analysis of slicing checks

In this paper, a new method of measuring slicing checks for flat-sliced veneers was evaluated. The method is based on image analysis of veneer cross-sections, having highlighted the slicing checks using surface staining. The segmentation of the checks consists of global thresholding followed by some morphological operations. The outputs of the algorithm are check depth ratio and check frequency. The method was tested on flat-sliced oak (Quercus robur L. and Quercus petraea (Matt). Liebl.) veneers of different thicknesses (1.5, 2.5, 3.5 and 4.5 mm). Two distinct wood qualities and two different cutting directions (lengthwise-sliced and plain-sliced veneers) were evaluated. The algorithm performance resulted in an overall accuracy of 85% enabling an accessible method for relatively fast and accurate measurements of slicing check characteristics in lamella cross-sections. Regression analysis indicated a lack of fixed bias but the presence of proportional bias with the presented method. Check measurements indicate that by varying cutting parameters, it is possible to achieve desired check characteristics independent of slicing thickness. The semi-automated slicing check detection method could benefit further research and optimisation of the slicing process parameters and pave the way towards industrial quality control of slicing checks. The intended area of application is veneer-laminated products for interior use with the focus on veneered wood flooring.

First results from the Sweeping Langmuir Probe (SLP) instrument on board PICASSO

&lt;p&gt;The Sweeping Langmuir Probe (SLP) instrument on board the Pico-Satellite for Atmospheric and Space Science Observations (PICASSO) has been developed at the Royal Belgian Institute for Space Aeronomy.&amp;#160; PICASSO, an ESA in-orbit demonstrator launched in September 2020, is a triple unit CubeSat orbiting at about 540 km altitude with 97 degrees inclination. The SLP instrument includes four independent cylindrical probes that are used to measure the plasma density and electron temperature as well as the floating potential of the spacecraft. Along the orbit of PICASSO the plasma density is expected to fluctuate over a wide range, from about 1e8/m&lt;sup&gt;3&lt;/sup&gt; at high latitude up to more than 1e12/m&lt;sup&gt;3&lt;/sup&gt; at low/mid latitude. SLP can measure plasma density from 1e8/m&lt;sup&gt;3&lt;/sup&gt; to 1e13/m&lt;sup&gt;3&lt;/sup&gt;. The electron temperature is expected to lie between approximately 1000 K and 10.000 K. Given the high inclination of the orbit, SLP will allow a global monitoring of the ionosphere. Using the traditional sweeping mode, the maximum spatial resolution is of the order of a few hundred meters for the plasma density, electron temperature and spacecraft potential. With the fixed-bias mode, the electron density can be measured with a spatial resolution of about 1.5 m. The main goals are to study the ionosphere-plasmasphere coupling, the subauroral ionosphere and corresponding magnetospheric features together with auroral structures and polar caps, by combining SLP data with other complementary data sources (space- or ground-based instruments). The first results from SLP will be presented.&lt;/p&gt;

Error simulation modeling to assess the effects of bias and precision on bilirubin measurements used to screen for neonatal hyperbilirubinemia

Objectives Error simulation models have been used to understand the relationship between analytical performance and clinical outcomes. We developed an error simulation model to understand the effects of method bias and precision on misclassification rate for neonatal hyperbilirubinemia using an age-adjusted risk assessment tool. Methods For each of 176 measured total bilirubin (TSBM) values, 10,000 simulated total bilirubin (TBS) values were generated at each combination of bias and precision conditions for coefficient of variation (CV) between 1 and 15%, and for biases between −51.3 μmol/L and 51.3 μmol/L (−3 and 3 mg/dL) fixed bias. TBS values were analyzed to determine if they were in the same risk zone as the TSBM value. We then calculated sensitivity and specificity for prediction of ≥75th percentile for postnatal age values as a function of assay bias and precision, and determined the rate of critical errors (≥95th percentile for age TSBM with <75th percentile TBS). Results A sensitivity >95% for predicting ≥75th percentile bilirubin values was observed when there is a positive fixed bias of greater than 17.1 μmol/L (1.0 mg/dL) and CV is maintained ≤10%. A specificity >70% for predicting <75th percentile bilirubin values was observed when positive systematic bias was 17.1 μmol/L (1 mg/dL) or less at CV ≤ 10%. Critical errors did not occur with a frequency >0.2% until negative bias was −17.1 μmol/L (−1 mg/dL) or lower. Conclusions A positive systematic bias of 17.1 μmol/L (1 mg/dL) may be optimal for balancing sensitivity and specificity for predicting ≥75th percentile TSB values. Negative systematic bias should be avoided to allow detection of high risk infants and avoid critical classification errors.

The Validity of an Updated Metabolic Power Algorithm Based upon di Prampero’s Theoretical Model in Elite Soccer Players

The aim of this study was to update the metabolic power (MP) algorithm (PV˙O2, W·kg−1) related to the kinematics data (PGPS, W·kg−1) in a soccer-specific performance model. For this aim, seventeen professional (Serie A) male soccer players (V˙O2max 55.7 ± 3.4 mL·min−1·kg−1) performed a 6 min run at 10.29 km·h−1 to determine linear-running energy cost (Cr). On a separate day, thirteen also performed an 8 min soccer-specific intermittent exercise protocol. For both procedures, a portable Cosmed K4b2 gas-analyzer and GPS (10 Hz) was used to assess the energy cost above resting (C). From this aim, the MP was estimated through a newly derived C equation (PGPSn) and compared with both the commonly used (PGPSo) equation and direct measurement (PV˙O2). Both PGPSn and PGPSo correlated with PV˙O2 (r = 0.66, p < 0.05). Estimates of fixed bias were negligible (PGPSn = −0.80 W·kg−1 and PGPSo = −1.59 W·kg−1), and the bounds of the 95% CIs show that they were not statistically significant from 0. Proportional bias estimates were negligible (absolute differences from one being 0.03 W·kg−1 for PGPSn and 0.01 W·kg−1 for PGPSo) and not statistically significant as both 95% CIs span 1. All variables were distributed around the line of unity and resulted in an under- or overestimation of PGPSn, while PGPSo routinely underestimated MP across ranges. Repeated-measures ANOVA showed differences over MP conditions (F1,38 = 16.929 and p < 0.001). Following Bonferroni post hoc test significant differences regarding the MP between PGPSo and PV˙O2/PGPSn (p < 0.001) were established, while no differences were found between PV˙O2 and PGPSn (p = 0.853). The new approach showed it can help the coaches and the soccer trainers to better monitor external training load during the training seasons.

Ordinary Least Products Regression: A Robust Statistical Tool for Assessing Agreement Between Measures Attended by High Variability

Background: Agreement (and disagreement) assessments are essential steps in the evaluation of new and existing methods. We aimed to provide a statistical approach to assess systematic disagreement between two measures/methods when both are attended by random error and high variability. Methods: We applied ordinary least products (OLP) regression and the Bland-Altman method in six simulated pairs of samples. In OLP regression, fixed bias defined if 95% confidence intervals (CIs) of the intercept did not include 0. Proportional bias was defined if 95% CIs of the slope did not include 1. As a comparator, we assessed fixed and proportional bias by the Bland-Altman method. Results: We found divergence between studied statistical method outcomes only for measures with low variability (coefficient of variation, CV < 25,0%). Conclusion: OLP regression is a simple and powerful tool for detecting systematic disagreement when the measures are attended by high variability, as well as behavioral variables.